A CASE-STUDY IN APPLYING NEURAL NETWORKS TO PREDICTING INSOLVENCY FORPROPERTY AND CASUALTY INSURERS

This paper presents a neural network artificial intelligence model dev eloped in cooperation with the Texas Department of Insurance as part o f an early warning system for predicting insurer insolvency. A feed-fo rward back-propagation methodology is utilised to compute an estimate of insurer propensity towards insolvency. The results are then applied to a collection of all Texas domestic property and casualty insurance companies which became insolvent between 1987 and 1990 and the goal o f predicting insolvency three years ahead of time. The results shaw hi gh predictability and generalisability of results for the purpose of i nsolvency prediction, suggesting that neural networks may be a useful technique for this and other purposes.